Leveraging LLMs for NER Task on Historical Literary Data in Urdu as a Low- Resource Right-to-Left Language

This study evaluates Large Language Models (LLMs) for Named Entity Recognition (NER) on a poetic form i.e., Marisya in the right to left Urdu script. The scarcity of annotated Urdu datasets by creating a human-annotated corpus is addressed and the performance of LLMs against the human-annotated corpus is evaluated.